Method and apparatus for low depth of field image segmentation

ABSTRACT

A method for extracting an object of interest from an image is provided. The method initiates with defining an image feature space based upon frequency information. Then, the image feature space is filtered to smooth both focused regions and defocused regions while maintaining respective boundaries associated with the focused regions and the defocused regions. The filtered image feature space is manipulated by region merging and adaptive thresholding to extract an object-of-interest. A computer readable media, an image capture device and an image searching system are also provided.

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority from: (1) U.S. ProvisionalPatent Application No. 60/419,303, filed Oct. 17, 2002, and entitled“Segmentation of Images with Low Depth-of-Field Using Higher OrderStatistics Test and Morphological Filtering by Reconstruction,” and (2)U.S. Provisional Patent Application No. 60/451,384, filed Feb. 28, 2003,and entitled “Automatic Segmentation of Low Depth-of-Field Image UsingMorphological Filters And Region Merging.” Each of these provisionalapplications is herein incorporated by reference in its entirety for allpurposes.

BACKGROUND OF THE INVENTION

[0002] 1. Field of the Invention

[0003] This invention relates generally to digital image technology andmore particularly to a method and apparatus for partitioning an imageinto homogeneous regions.

[0004] 2. Description of the Related Art

[0005] Automatic image segmentation is one of the most challengingproblems in computer vision. The objective of image segmentation is topartition an image into homogeneous regions. Depth of Field (DOF) refersto the distance from the nearest to the furthest point of perceived“sharp” focus in a picture. Low DOF is a photographic technique commonlyused to assist in understanding depth information within a 2 dimensionalphotograph. Low DOF generally refers to a condition when an object ofinterest (OOI) is in sharp focus and the background objects are blurredto out of focus. FIGS. 1A through 1C are exemplary illustrations of lowDOF images. The butterfly of FIG. 1A is highly focused, i.e., the objectof interest, while the background is defocused. The soccer player andsoccer ball of FIG. 1B are the objects of interest, since each is highlyfocused, while the background is defocused. Similarly, with reference toFIG. 1C, the bird is highly focused while the remainder of the image isdefocused. Segmentation of images with low DOF is applicable to numerousapplications, e.g., image indexing for content-based retrieval,object-based image compression, video object extraction, 3D microscopicimage analysis, and range segmentation for depth estimation.

[0006] Assuming sharply focused regions contain adequate high frequencycomponents, it should be possible to distinguish the focused regionsfrom the low DOF image by comparing the amount of the high frequencycontent. There are two approaches for the segmentation of the low DOFimages: edge-based and region-based approaches. The edge-based methodextracts the boundary of the object by measuring the amount of defocusat each edge pixel. The edge-based algorithm has demonstrated accuracyfor segmenting man-made objects and objects with clear boundary edges.However, this approach often fails to detect boundary edges of thenatural object, yielding disconnected boundaries.

[0007] On the other hand, the region-based segmentation algorithms relyon the detection of the high frequency areas in the image. Here, areasonable starting point is to measure the degree of focus of eachpixel by computing the high frequency components. To this end, severalmethods have been used, such as spatial summation of the squaredanti-Gaussian (SSAG) function, variance of wavelet coefficients in thehigh frequency bands, a multi-scale statistical description of highfrequency wavelet coefficients, and local variance, etc. Exploiting highfrequency components alone often results in errors both in focused anddefocused regions. In defocused regions, despite blurring due to thedefocusing, there could be busy texture regions in which high frequencycomponents are still strong enough. These regions are prone to bemisclassified as focused regions. Conversely, focused regions withnearly constant gray levels may also generate errors in these regions.Thus, relying only on the sharp detail of the OOOI can be a limitationfor the region-based DOF image segmentation approach. Furthermore, themulti-scale approaches tend to generate jerky boundaries even thoughrefinement algorithms for high resolution classification areincorporated.

[0008]FIG. 2 is a schematic diagram of the optical geometry of a typicalimage capture device such as a camera. Lens 100 has the disadvantagethat it only brings to focus light from points at a distance-z given bythe familiar lens equation: $\begin{matrix}{{{\frac{1}{z^{\prime}} + \frac{1}{- z}} = \frac{1}{f}},} & (2)\end{matrix}$

[0009] where z′ is the distance of image plane 102 from lens 100 and fis the focal length. Points at other distances are imaged as littlecircles. The size of the blur circle can be determined as follows: Apoint at distance −{overscore (z)} is imaged at a point {overscore (z)}′from the lens, where 1/{overscore (z)}′+1/−{overscore (z)}=1/f,

[0010] and so $\begin{matrix}{( {{\overset{\_}{z}}^{\prime} - z^{\prime}} ) = {\frac{f}{( {\overset{\_}{z} + f} )}\frac{f}{( {z + f} )}{( {\overset{\_}{z} - z} ).}}} & (3)\end{matrix}$

[0011] If image plane 102 is situated to receive correctly focusedimages of object at distance −z, then points at distance −{overscore(z)} will give rise to blur circles of diameter${\frac{d}{z^{\prime}}{{{\overset{\_}{z}}^{\prime} - z^{\prime}}}},$

[0012] where d represents the diameter of lens 100. The depth of field(DOF) is the range of distances over which objects are focused“sufficiently well,” in the sense that the diameter of the blur circleis less than the resolution of the imaging device. The DOF depends, ofcourse, on what sensor is used, but in any case it is clear that thelarger the lens aperture, the less the DOF. Of course, errors infocusing become more serious when a large aperture is employed. As shownin FIG. 2, d_(f) 104 and d_(r) 106 represent the front and rear limits,respectively, of the “depth of field.” With low DOF, the diameter ofblur circle becomes small, thus only the OOI is in sharp focus, whereasobjects in background are blurred to out of focus. Additionally,segmentation techniques based upon color and intensity informationsuffer from poor extraction results.

[0013] As a result, there is a need to solve the problems of the priorart to provide a method and apparatus for segmenting an image associatedwith a low depth of field such that the object of interest may beextracted from the background accurately and efficiently.

SUMMARY OF THE INVENTION

[0014] Broadly speaking, the present invention fills these needs byproviding a method and system for transforming the image data tofrequency based image data and simplifying the frequency based imagedata in order to more effectively extract an object of interest (OOI)from the image data. It should be appreciated that the present inventioncan be implemented in numerous ways, including as a method, a system,computer code or a device. Several inventive embodiments of the presentinvention are described below.

[0015] In one embodiment, a method for partitioning image data isprovided. The method initiates with defining an image feature spacebased upon frequency information. Then, the data of the image featurespace is simplified by morphological tools. Next, a region of thefiltered image feature space is assigned as an initial object ofinterest. Here, the region is referred to as a seed region which isassociated with the highest value assigned to regions of the filteredimage feature space. Each of the regions of the filtered image space isassociated with a substantially constant frequency level. Then, theboundary of the initial OOI of the filtered image feature space isupdated through a region merging technique. Adaptive thresholding isthen conducted to determine a size of the initial object of interestrelative to an image data size.

[0016] In another embodiment, a method of image segmentation isprovided. The method initiates with generating a higher order statistic(HOS) map from image data. Then, the HOS map is simplified. Next, aboundary associated with a focused region of the modified HOS map isdetermined. Then a final segmentation of the focused region isdetermined through adaptive thresholding.

[0017] In yet another embodiment, a method for extracting an object ofinterest from an image is provided. The method initiates with definingan image feature space based upon frequency information. Then, the imagefeature space is filtered to smooth both focused regions and defocusedregions while maintaining respective boundaries associated with thefocused regions and the defocused regions.

[0018] In still yet another embodiment, a computer readable mediumhaving program instructions for image segmentation is provided. Thecomputer readable medium includes program instructions for generating ahigher order statistic (HOS) map from image data. Program instructionsfor modifying the HOS map are included. Program instructions fordetermining a boundary associated with a focused region of the modifiedHOS map are provided. Program instructions for determining a finalsegmentation of the focused region based upon a size of a valueassociated with the focused region relative to an image data size arealso included.

[0019] In another embodiment, an image capture device is provided. Theimage capture device includes a lens configured to focus objects withina depth of field (DOF). An image recording assembly is included with theimage capture device. The image recording assembly is configured togenerate a digital image including the objects within the DOF from imageinformation received through the lens. The image recording assembly iscapable of generating a higher order statistic (HOS) map of the digitalimage in order to extract the objects within the DOF from the digitalimage.

[0020] In yet another embodiment, an image searching system is provided.The image searching system includes an image capture device having alens configured to focus objects within a depth of field (DOF). An imageextraction assembly in communication with the image capture device isincluded. The image extraction assembly is configured to extract theobjects within the DOF. An image content retrieval system incommunication with the image extraction assembly is included. The imagecontent retrieval system is configured to receive data corresponding tothe objects within the DOF. The image content retrieval system isfurther configured to identify a match between the OOIs of the receiveddata and gathered image data.

[0021] Other aspects and advantages of the invention will becomeapparent from the following detailed description, taken in conjunctionwith the accompanying drawings, illustrating by way of example theprinciples of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The present invention will be readily understood by the followingdetailed description in conjunction with the accompanying drawings, andlike reference numerals designate like structural elements.

[0023]FIGS. 1A through 1C are exemplary illustrations of low DOF images.

[0024]FIG. 2 is a schematic diagram of the optical geometry of a typicalimage capture device such as a camera.

[0025] FIGS. 3A-C represent an original image and associated imagefeature spaces illustrating the effectiveness of the application ofhigher order statistics in accordance with one embodiment of theinvention.

[0026] FIGS. 4A-4E represent a pictorial illustration of the HOS map(4B) of a low DOF image (4A), the application of the morphologicalfilters by reconstruction to the HOS map (4C), the result of theapplication of region merging (4D) and the result of the application ofadaptive thresholding (4E) in accordance with one embodiment of theinvention.

[0027] FIGS. 5A-C represent a pictorial illustration of the regionmerging technique in accordance with one embodiment of the invention.

[0028] FIGS. 6-1 through 6-4 provide four series of experimental resultsfor each of the steps associated with the segmentation technique of theembodiments described herein.

[0029] FIGS. 7-1 through 7-4 illustrate four series of images enablingthe comparison of results of existing segmentation techniques with theresults generated by the application of the embodiments describedherein.

[0030]FIG. 8 is a flow chart diagram of the method operations forextracting an object of interest from an image in accordance with oneembodiment of the invention.

[0031]FIG. 9 is a simplified schematic diagram of an image capturedevice having circuitry configured to extract an object of interestassociated with a low depth of field image in accordance with oneembodiment of the invention.

[0032]FIG. 10 is a simplified schematic diagram of an image searchingsystem in accordance with one embodiment of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0033] An invention is described for a system, apparatus and method forextracting an object of interest (OOI) from a low depth of field (DOF)image. It will be apparent, however, to one skilled in the art, in lightof the following description, that the present invention may bepracticed without some or all of these specific details. In otherinstances, well known process operations have not been described indetail in order not to unnecessarily obscure the present invention.FIGS. 1A-C and 2 are described in the “Background of the Invention”section. The term about as used to herein refers to +/−10% of thereferenced value.

[0034] The embodiments of the present invention provide a method and asystem for separating sharply focused objects of interest (OOI)associated with a low depth of field (DOF) image from other foregroundor background objects in the image. Thus, an image having a low DOF maybe partitioned into a focused region and a defocused region. Thefrequency information associated with the image data is used topartition the image rather than the color or intensity information.Unlike intensity or color image segmentation, in which regions arediscovered using properties of the intensity, texture or color, focuscue may play the most important role for the automatic extraction of thefocused OOI. The low DOF image is transformed into the appropriatefeature space for the segmentation. In one embodiment, thetransformation to the appropriate feature space is accomplished bycomputing higher order statistics (HOS) for all pixels in the low DOFimage to generate a HOS map. The HOS map is then simplified, i.e.,modified, through morphological filtering by reconstruction as describedbelow. The boundaries of the OOI are defined and updated through regionmerging. Then, the final OOI is determined through adaptivethresholding. Thus, an accurate extraction of the OOI associated withthe low DOF image data is provided for a host of applications.

[0035] In order to model defocusing of a focused image, blurring effectby defocusing has been often described by a 2-D Gaussian function:$\begin{matrix}{{G_{\sigma}( {x,y} )} = {\frac{1}{2\quad \pi \quad \sigma^{2}}{\exp ( {- \frac{x^{2} + y^{2}}{2\quad \sigma^{2}}} )}}} & (1)\end{matrix}$

[0036] where σ is a filter scale or spread parameter which controls theamount of defocusing. Thus, a defocused image I_(d)(x,y) can be modeledas the linear convolution of a focused image I_(f)(x,y) and a Gaussianfunction G_(σ)(x,y):

I_(d)(x,y)=G_(σ)(x,y)*I_(f)(x,y)  (4)

[0037] As shown in Equation (4), since the defocused image is low passfiltered, high frequency components in the image are removed or reduced.Assuming sharply focused regions contain adequate high frequencycomponents, it should be possible to distinguish the focused regionsfrom the low DOF image by comparing the amount of the high frequencycontent.

[0038] Let R represent a set of pixels, R {(k,l); 1≦k≦K, 1≦l≦L}, wherethe image size is K×L. The goal is to partition R into sharply focusedobjects-of-interest (OOI), which will be denoted by OOI, and remainingregions, expressed by OOI^(c).

[0039] Let P={R_(i), i∈{1, . . . ,N}} denote a partition of R. The OOIof an image is defined as follows: $\begin{matrix}{{OOI} = {\bigcup\limits_{i = 1}^{N_{ooi}}{R_{i}.}}} & (5)\end{matrix}$

[0040] where R_(i) is the i-th connected region and N_(ooi) denotes thenumber of regions belonging to OOI. In other words, OOI represents thefocused objects of interest, composed of N_(ooi) regions of P. Equation(5) naturally allows for the definition of multiple OOIs, i.e., OOI canbe composed of separated sub-OOIs.

[0041] An initial step towards segmentation consists of transforming theinput low DOF image, I, into the most appropriate feature space. Itshould be appreciated that the choice of the feature space may depend onthe application that the segmentation algorithm is aimed at. Forinstance, the feature space may represent the set of waveletcoefficients, or local variance image field.

[0042] In one embodiment, higher order statistics (HOS) is applied forfeature space transformation. More specifically, the fourth-ordermoments are calculated for all pixels in the image. It should beappreciated that the fourth order moments have an ability to suppressGaussian noise, thereby enhancing the ultimate accuracy of theextraction of an OOI. The fourth-order moment at (x,y) is defined asfollows: $\begin{matrix}{{{\hat{m}}^{(4)}( {x,y} )} = {\frac{1}{N_{\eta}}{\sum\limits_{{({s,t})} \in {\eta {({x,y})}}}( {{I( {s,t} )} - {\hat{m}( {x,y} )}} )^{4}}}} & (6)\end{matrix}$

[0043] where η(i,j) is a set of pixels centering at (i,j), {circumflexover (m)}(x,y) is the sample mean of I(x,y) (i.e.,$( {{i.e.},{{\hat{m}( {x,y} )} = {\frac{1}{N_{\eta}}{\sum\limits_{{({s,t})} \in {\eta {({x,y})}}}{I( {s,t} )}}}}} ),$

[0044] and N_(η) is a size of η. Since the dynamic range of thefourth-order moment values is extremely large, the value for each pixelis down scaled and limited by 255 such that each pixel takes a valuefrom [0, 255]. The outcome image is called a HOS map and it is definedas:

HOS(x,y)=min(255,{circumflex over (m)} ⁽⁴⁾(x,y)/100)  (7)

[0045] Applying Equation (7) for all pixels, results in a HOS map,O={HOS(x,y);(x,y)∈R}.

[0046] FIGS. 3A-C represent an original image and associated imagefeature spaces illustrating the effectiveness of the application ofhigher order statistics in accordance with one embodiment of theinvention. FIG. 3C illustrates a HOS map generated as described hereinfrom the low DOF image depicted in FIG. 3A. Comparing it to localvariance map shown in FIG. 3B, it can be seen that the HOS map of FIG.3C yields denser and higher values in the focused areas whilesuppressing noise in the defocused regions. That is, OOI 110 c isillustrated as having a more distinct solid white area than image 110 b.

[0047] It should be appreciated that the feature space transformationdescribed above, i.e., the application of a HOS calculation to define aHOS map, enables the definition of a more adequate feature space toeventually be exploited for image segmentation. In one embodiment, theHOS map transformed from the low DOF image has gray levels from 0 to255. A higher value within the 0-255 range corresponds to a higherpotential for focused regions. Since focused smooth regions are not aswell detected by the HOS calculation, while some defocused regions maygenerate noise, a proper tool for HOS map modification is needed toremove small dark and bright patches in both focused and defocusedregions, respectively.

[0048] Mathematical morphology is well known as an approach forsmoothing noisy gray-level images by a determined composition of openingand closing with a given structuring element. A number of morphologicaltools rely on two basic sets of transformations known as erosion anddilation. Let B denote a window or flat structuring element and letB_(x,y) be the translation of B so that its origin is located at (x,y).Then, the erosion ε_(B)(O) of a HOS map O by the structuring element Bis used in constructing a morphological filter for image simplification$\begin{matrix}{{{ɛ_{B}(O)}( {x,y} )} = {\min\limits_{{({k,l})} \in B_{x,y}}{{{HOS}( {k,l} )}.}}} & (8)\end{matrix}$

[0049] Similarly, the dilation $\begin{matrix}{{{\delta_{B}(O)}( {x,y} )} = {\max\limits_{{({k,l})} \in B_{x,y}}{{{HOS}( {k,l} )}.}}} & (9)\end{matrix}$

[0050] Elementary erosions and dilations allow the definition ofmorphological filters such as morphological opening and closing:

[0051] Morphological opening, γ_(B)(O) and closing, φ_(B)(O) are givenby

γ_(B)(O)=δ_(B)(ε_(B)(O)),

φ_(B)(O)=ε_(B)(δ_(B)(O))  (10)

[0052] The morphological opening operator γ_(B)(O) applies an erosionε_(B)(•) followed by a dilation δ_(B)(•) Erosion leads to darker imagesand dilation to brighter images. A morphological opening (or closing)simplifies the original signal by removing the bright (or dark)components that do not fit within the structuring element B. Thus,morphological operators can be directly applied to binary image withoutany change.

[0053] One feature of the morphological filters is that the filters donot allow for a perfect preservation of the boundary information of anobject. Accordingly, this may be a drawback in some instances. Toovercome this drawback, filters by reconstruction may be employed.Although similar in nature to morphological opening and closing filters,the filters by reconstruction rely on different erosion and dilationoperators, making their definitions slightly more complicated. Theelementary geodesic erosion ε⁽¹⁾(O,O_(R)) of size one of the originalimage O with respect to the reference image O_(R) is defined as:

ε⁽¹⁾(O,O_(R))(x,y)=max {ε_(B)(O)(x,y),O_(R)(x,y)}  (11)

[0054] and the dual geodesic dilation δ⁽¹⁾(O,O_(R)) of O with respect toO_(R) is given by:

δ⁽¹⁾(O,O_(R))(x,y)=min {δ_(B)(O)(x,y),O_(R)(x,y)}  (12)

[0055] Thus, the geodesic dilation δ⁽¹⁾(O,O_(R)) dilates the image Ousing the classical dilation operator δ_(B)(O). Dilated gray values aregreater than or equal to the original values in O. However, geodesicdilation limits these to the corresponding gray values of R, thereference image, as discussed below.

[0056] Geodesic erosions and dilations of arbitrary size are obtained byiterating the elementary versions ε⁽¹⁾(O,O_(R)) and δ⁽¹⁾(O,O_(R))accordingly. For example, the geodesic erosion (dilation) of infinitesize, which is so-called reconstruction by erosion (by dilation) isgiven by the following:

[0057] Reconstruction by Erosion:

φ^((rec))(O,O_(R))=ε^((∞))(O,O_(R))=ε⁽¹⁾∘ε⁽¹⁾∘ . . .∘ε⁽¹⁾(O,O_(R))  (13)

[0058] Reconstruction by Dilation:

γ^((rec))(O,O_(R))=δ^((∞))(O,O_(R))=δ⁽¹⁾∘δ⁽¹⁾∘ . . .∘δ⁽¹⁾(O,O_(R))  (14)

[0059] It should be appreciated that both φ^((rec))(O,O_(R)) andγ^((rec))(O,O_(R)) will reach stability after a certain number ofiterations. The two simplification filters, morphological opening byreconstruction, γ^((rec))(ε_(B)(O),O) and morphological closing byreconstruction, φ^((rec))(δ_(B)(O),O), may be thought of as merelyspecial cases of γ^((rec))(O,O_(R)) and φ^((rec))(O,O_(R)) in oneembodiment.

[0060] Similar to morphological opening, morphological opening byreconstruction first applies the basic erosion operator ε_(B)(O) toeliminate bright components that do not fit within the structuringelement B. However, instead of applying just a basic dilationafterwards, the contours of components that have not been completelyremoved are restored by the reconstruction by dilation operatorγ^((rec))(•,•) The reconstruction is accomplished by choosing O as thereference image R, which guarantees that for each pixel the resultinggray-level will not be higher than that in the original image O.

[0061] In one embodiment of the schemes described herein, morphologicalclosing-opening by reconstruction is applied to the HOS map as asimplification tool. It should be appreciated that one strength of themorphological closing-opening by reconstruction filter is that it fillssmall dark holes and removes small bright isolated patches, whileperfectly preserving other components and their contours. Of course, thesize of removed components depends on the size of the structuringelement.

[0062] FIGS. 4A-4C represent a pictorial illustration of the HOS map ofa low DOF image and the application of the morphological filters byreconstruction to the HOS map in accordance with one embodiment of theinvention. FIGS. 4D and 4E are explained further below. FIG. 4A is anexemplary low DOF image. FIG. 4B is the resulting HOS map generated bycalculating a HOS for each pixel value of the image data of FIG. 4A. Ascan be seen, FIG. 4B includes dark patches within the object ofinterest, which is defined as the two soccer players and soccer ball 114a. Additionally bright patches exist in the defocused region, such asbright patches in region 116 b. Through the simplification of the HOSmap, e.g., applying morphological filters by reconstruction to the HOSmap of FIG. 4B, the small dark patches within the focused region areremoved. That is FIG. 4C represents a simplified HOS map, where thesimplification is achieved through the application of morphologicalfilters by reconstruction as described above. For example, soccer ball114 c does not include the dark patches of soccer ball 114 b. Likewise,the small bright patches in the defocused region are removed whencomparing FIG. 4C to FIG. 4B. Accordingly, as shown in FIG. 4C, thefocused smooth regions are well covered while the scattered smallregions are removed by the filter.

[0063] For a typical morphological segmentation technique, which focuseson partitioning an image or scene into homogeneous regions in terms ofintensity, the simplification by morphological filter may be followed bymarker extraction and watershed algorithm. The marker extraction stepselects initial regions, for example, by identifying large regions ofconstant gray level obtained in the simplification step, where thesimplification step may be the application of the morphological filtersdiscussed above. After marker extraction, a large number of pixels arenot assigned to any region. These pixels correspond to uncertainty areasmainly concentrated around the contours of the regions. Assigning thesepixels to a given region can be viewed as a decision process thatprecisely defines the partition or segment. One morphological decisiontool is the watershed algorithm, which labels pixels in a similarfashion to region growing techniques.

[0064] Unlike the conventional intensity-based segmentation focusing onpartition of the image, the task of the low DOF image segmentation is toextract a focused region (i.e., OOI) from the image. Similar focusedregions may be merged by using seed regions, which are highly probableregions of OOI as described below.

[0065] In one embodiment, every flat zone is initially treated as aregion regardless of size, which means even one pixel zone may become aregion. Then, it is assumed that regions associated with the highestvalue belong to an initial OOI, while regions having values from 0through T_(L) belong to initial OOI^(c). With reference to FIG. 4C, thesimplified HOS map usually contains uncertainty regions, e.g., region112 c, with values v, T_(L)<v<255, which are assigned to either OOI orOOI^(c). One skilled in the art will appreciate that OOI refers to anobject of interest, while OOI is a reference for mathematicalexpressions. Such an assignment updates the OOI and may be conducted byusing bordering information between uncertainty region and current OOI,OOI_(n) (i.e., OOI in the nth iteration). Therefore, an algorithm thatassigns an ith uncertainty region R_(n,i) in the nth iteration toOOI_(n) by computing normalized overlapped boundary (nob) performs thisfunction as discussed below.

[0066] Given an partition P_(n), the normalized overlapped boundary(nob) between ith uncertain region R_(n,i)∈P_(n) and the OOI_(n), isgiven by $\begin{matrix}{{{nob}_{n,i} = \frac{{cardinal}( {{BR}_{n,i}\bigcap{OOI}_{n}} )}{{cardinal}( {BR}_{n,i} )}},} & (15)\end{matrix}$

[0067] where the set of boundary pixels of R_(n,i) is defined as${BR}_{n,i} = {\{ {x \notin R_{n,i}} \middle| {{\min\limits_{r \in R_{n,i}}{{r - x}}} \leq T_{b}} \}.}$

[0068] It should be appreciated that Equation 15 yields a value of zerowhen the uncertainty region R_(n,i) is not adjacent to OOI_(n) and avalue of one when the R_(n,i) is perfectly enclosed by OOI_(n)'sboundary pixels. Accordingly, a value between 0 and 1 may be used todecide for the assignment of the uncertainty regions in P_(n) to eitherOOI_(n) or OOI_(n) ^(c) in one embodiment of the invention. Thethreshold value, T_(b) for defining boundary pixels of a region issimply set to be 1 in another embodiment of the invention. Obviously,the uncertainty region R_(n,i)∈P_(n) belongs to either OOI_(n) or anyother regions. In hypothesis-testing terms,

H₀:R_(n,i)

OOI_(n); H₁:₀ ^(c)  (16)

[0069] The normalized overlapped boundary (nob) may be modeled as acontinuous random variable nob (random variable should be in bold),taking values of nob in [0, 1]. If nob_(n,i) is larger than a thresholdvalue, the region R_(n,i) is merged to OOI_(n). Then, the partitionP_(n) and OOI_(n) are updated, yielding an increasing sequence ofOOI_(n) and eventually converging to OOI. In one embodiment, a startingpoint for finding the threshold value T_(nob) is calculated by thelikelihood ratio test as follows (It should be appreciated that theiteration index n has been dropped in order to simplify the notation.):

[0070] Assign R_(i) to ooi if P(ooi|nob_(i))>P(ooi^(c)|nob_(i));otherwise assign to ooi^(c).

[0071] where ooi represents the class for the OOI with prior probabilityP(ooi), and ooi^(c) denotes the class for the non-OOI with priorprobability P(ooi^(c))=1−P(ooi). P(ooi|nob_(i)) and P(ooi^(c)|nob_(i))represents the a posteriori conditional probabilities that correspond toH₀ and H₁, respectively. If Bayes theorem is applied on both sides ofthe expression and the terms are rearranged as shown below:$\begin{matrix}{{\frac{p( {nob}_{i} \middle| {ooi} )}{p( {nob}_{i} \middle| {ooi}^{c} )}\begin{matrix}\overset{H_{0}}{>} \\\underset{H_{1}}{<}\end{matrix}\frac{P( {ooi}^{c} )}{P({ooi})}},} & (17)\end{matrix}$

[0072] the left-hand ratio is known as the likelihood ratio, and theentire equation is often referred to as the likelihood ratio test. Sincethe test is based on choosing the region class with maximum a posterioriprobability, the decision criterion is called the maximum a posteriori(MAP) criterion. The decision criterion may also be referred to as theminimum error criterion, since on the average, this criterion yields theminimum number of incorrect decisions. Furthermore, as the object ofinterest and background may have any size and shape, equal priors may beassumed, i.e., (P(ooi)=P(ooi^(c))), and thus, the expression reduces tothe maximum likelihood (ML) criterion: $\begin{matrix}{\frac{p( {nob}_{i} \middle| {ooi} )}{p( {nob}_{i} \middle| {ooi}^{c} )}\begin{matrix}\overset{H_{0}}{>} \\\underset{H_{1}}{<}\end{matrix}1.} & (18)\end{matrix}$

[0073] Modeling the class-conditional probability density functions byexponential distributions results in:

p(nob_(i)|ooi^(c))=λ₁ e ^(−λ) ^(₁) ^(nob) ^(_(i)) u(nob_(i))

p(nob_(i)|ooi)=λ₂ e ^(−λ) ^(₂) ^((1−nob) ^(_(i)) ⁾ u(1−nob_(i))  (19)

[0074] where u(x) denotes the step function. The above distributionsapproximately model the real data: p(nob_(i)|ooi) would have high valuesaround nob=1 and rapidly decay as nob_(i)→0, while p(nob_(i)|ooi^(c))would have high values around nob_(i)=0 and rapidly decay as nob_(i)→1.Finally, optimal threshold for nob_(i) can be obtained by rearrangingEquations 18 and 19 as depicted below: $\begin{matrix}{{{{nob}_{i}\begin{matrix}\overset{H_{0}}{>} \\\underset{H_{1}}{<}\end{matrix}\frac{\lambda_{2}}{\lambda_{1} + \lambda_{2}}} + \frac{\ln ( {\lambda_{1}/\lambda_{2}} )}{\lambda_{1} + \lambda_{2}}} = {T_{nob}.}} & (20)\end{matrix}$

[0075] The parameters λ₁ and λ₂ can be estimated from the actual data.However, if symmetry between the exponential distributions is assumed(λ₁=λ₂), the expression for the optimal threshold may be approximatedand simplified as $\begin{matrix}{T_{nob} = {{\frac{\lambda_{2}}{\lambda_{1} + \lambda_{2}} + \frac{\ln ( {\lambda_{1}/\lambda_{2}} )}{\lambda_{1} + \lambda_{2}}} \approx {\frac{1}{2}.}}} & (21)\end{matrix}$

[0076] Hence, if nob_(i) is larger than T_(nob), the R_(i) is merged toOOI and OOI is updated. This process is iterated until no mergingoccurs. It should be appreciated that a value of {fraction (1/2)} is oneexemplary value and the invention is not limited to a value of {fraction(1/2)}, as any suitable value for T_(nob) may be selected.

[0077] FIGS. 5A-C represent a pictorial illustration of the regionmerging technique in accordance with one embodiment of the invention. InFIG. 5A, nob_(i) is greater than T_(nob), thus R_(i) 122 merges intoOOI, while R_(k) 126 does not since nob_(k) is less than T_(nob). Inother words, the shared boundary between R_(i) 122 and OOI₀ 120 a isgreater than {fraction (1/2)} of the entire boundary of R_(i) 122,thereby resulting in the merging of R_(i) 122 into OOI₀ 120 a to defineOOI₁ 120 b of FIG. 5B. As the shared boundary between R_(k) 126 and OOI₀120 a is less than ½, R_(k) 126 is not merged into OOI₀ 120 a. Asmentioned above, T_(nob) may be any suitable value between 0 and 1,inclusive. In the next iteration, as illustrated in FIG. 5B, R_(j) 124 amerges into OOI₁ 120 b since nob_(j)>T_(nob), resulting in OOI₂ 120 c ofFIG. 5C. In order to expedite the process, very small regions can bemerged to the neighbor region with the nearest value in advance, in oneembodiment of the invention. For example, R_(j) 124 a may be merged intoregion R_(i) 122 as an initial step. FIG. 4D illustrates the results ofregion merging being applied to the simplified HOS map of FIG. 4C. Forexample, region 112 c of FIG. 4C is merged into OOI 118 of FIG. 4D byapplying the region merging technique described above.

[0078] A final decision of a size associated with the focused region(i.e., OOI) is conducted by adaptive thresholding. The adaptivethresholding decision may be based on the assumption that OOI occupies areasonable portion of the image. Starting at T_(A)=255, the thresholdvalue is decreased until the size of OOI becomes larger than about 20%of image size. For instance, with reference to FIG. 5C, R_(k) 126 maynot be decided to be as an OOI, since the size of OOI₂ 120 c is largerthan about 20% of the image size. However, if the size of OOI₂ 120 c isless than about 20% of the image size R_(k) 126 may be considered a partof the OOI. It should be appreciated that the invention is not limitedto a value of 20% of the image size for adaptive thresholding, as anysuitable value of the size of the OOI relative to the image size may beselected here. Referring to FIG. 4E, the adaptive thresholding techniquemay be applied to FIG. 4D, in order to yield the image of FIG. 4E.

[0079] The embodiments discussed herein have been implemented and testedon low DOF images selected from the JPEG compressed COREL™ CD-ROM imagecollection. Color images were first transformed into gray level imagesfor this test. None of the test images had homogeneous defocused regionsfor this test. A neighborhood of size 3 by 3 for η was used in Equation6 defined above. The threshold value T_(L) was set to be 20 in thetests. One skilled in the art will appreciate that one of the mostimportant parameters is the size of the structuring element (SE) of themorphological filter. The size was set to be 31×31 for all experimentsexcept the image shown in FIG. 4A. Since the size of soccer ball 114 ashown in FIG. 4A is too small, the ball is removed by the filter when31×31 of SE is applied. For better subjective result, 21×21 of SE wasemployed for FIG. 4A only.

[0080] FIGS. 6-1 through 6-4 provide four series of experimental resultsfor each of the steps associated with the segmentation technique of theembodiments described herein. The first image of each series is a lowDOF image. The second image of each series is a HOS map generated fromthe respective low DOF image. The third image of each series is asimplified HOS map, where a morphological filter by reconstruction hasbeen applied to each respective HOS map. The fourth image of each seriesillustrates images that have had region merging applied to therespective simplified HOS map. The fifth image of each series is animage to which adaptive thresholding has been applied to the respectivefourth image of each series. Thus, the fifth image represents theextracted OOI resulting from the application of the embodimentsdescribed herein.

[0081] FIGS. 7-1 through 7-4 illustrate four series of images enablingthe comparison of results of segmentation techniques with the resultsgenerated by the application of the embodiments described herein. Thefirst image of each series is a low DOF image. The second image of eachseries illustrates results from the multi-scale approach based on highfrequency wavelet coefficients and their statistics. The third image ofeach series illustrates results from the application of the localvariance scheme with Markov Random Field (MRF) model based segmentation.The fourth image of each series illustrates the results from theapplication the scheme described herein. As demonstrated by theillustrations, the results obtained from the second series of images areblocky due to block-wise initial classification, even though arefinement algorithm for high resolution classification wasincorporated. The algorithm utilized for the third image of each series,due to its smoothness constraint adopted in the MRF model, results inadjacent non-OOI regions tending to be connected. The proposed schemeillustrated in the fourth image of each series yields more accurateresults over various images with low DOF. For comparison purposes, thefifth image of each series provides a reference generated by humanmanual segmentation.

[0082] The segmentation performance of the proposed algorithm may alsobe evaluated by using objective criterion. A pixel-based qualitymeasure, which was proposed to evaluate the performances of video objectsegmentation algorithms may be used to provide the objective criterion.The spatial distortion of the estimated OOI from the reference OOI isdefined as $\begin{matrix}{{{d( {O^{est},O^{ref}} )} = \frac{\sum\limits_{({x,y})}{{O^{est}( {x,y} )} \otimes {O^{ref}( {x,y} )}}}{\sum\limits_{({x,y})}{O^{ref}( {x,y} )}}},} & (22)\end{matrix}$

[0083] where O^(est) and O^(ref) are the estimated and reference binarymasks, respectively, and {circle over (X)} is the binary “XOR”operation. Table 1 below provides the spatial distortion measures of theresults from 1) the variance of wavelet coefficients in the highfrequency bands of the second image in each series of FIGS. 7-1 through7-4, 2) the local variance scheme of the third image in each of theseries of FIGS. 7-1 through 7-4, and 3) the proposed scheme representedby the fourth image in each of the series of FIGS. 7-1 through 7-4.Reference maps are obtained by manual segmentation, as shown inrespective fifth images of FIGS. 7-1 through 7-4. For the binary “XOR”operation, pixels on OOI are set to be one, otherwise zero. As shown inTable 1, the scheme representing the embodiments described herein haslower distortion measures than those from the other methods and thesemeasure are well matched with subjective evaluation. TABLE 1 ImageSecond image of Third image of Fourth image of Series FIGS. 7-1-4 FIGS.7-1-4 FIGS. 7-1-4 7-1 0.1277 0.1629 0.0354 7-2 0.2872 0.4359 0.1105 7-30.2138 0.1236 0.1568 7-4 0.3134 0.2266 0.1709

[0084]FIG. 8 is a flow chart diagram of the method operations forextracting an object of interest from an image in accordance with oneembodiment of the invention. The method initiates with operation 140.Here, an image feature space is defined. The image feature space isbased upon frequency information as described above with reference tohigher order HOS being applied to each pixel of an image associated withthe image feature space. The method then advances to operation 142 wherethe image is filtered. Morphological filters by reconstruction areapplied to filter the image space in accordance with one embodiment ofthe invention. As described above, the morphological filters simplifythe image space. That is, holes and isolated patches associated witheither the focused region or defocused region are removed through themorphological filters as described with reference to FIGS. 4A-4E. In oneembodiment, initial OOIs are produced by identifying the interior ofhomogeneous regions. The areas with the highest values in the simplifiedHOS map may act as seed regions for the initial OOI. The values arebased upon the frequency level of the homogenous region in oneembodiment.

[0085] The method of FIG. 8 then moves to operation 144 where regionmerging is performed, i.e., a boundary of the object of interest isdetermined. First, every flat zone is treated as a region regardless ofits size, which means even one pixel zone can become a region. Then, anassumption is made that regions associated with the highest value v_(h)belong to initial OOI while regions having values from 0 through T_(L)belong to initial OOI^(c). For example, in FIG. 4-(c), the simplifiedHOS map contains uncertainty regions with values (T_(L),v_(h)), wherev_(h) is equal to 255. Those uncertainty regions are assigned to eitherOOI or OOI^(c). Such assignment is iteratively conducted by consideringbordering relationship between uncertainty region and current OOI,OOI_(n) (i.e., OOI at the nth iteration). In one embodiment, regionmerging is applied through the calculation of the normalized overlapboundary (nob) as discussed above with reference to FIGS. 5A-5C. Themethod then proceeds to operation 148 where a final size of the objectof interest is defined. Here, adaptive thresholding may be used todetermine the final size of the object of interest, as discussed abovewith reference to FIGS. 4E and 5C. That is, if a size associated withthe object of interest is less than a defined percentage of the totalimage size, then the object of interest is expanded until the size ofobject of interest achieves the defined percentage. In one embodiment,the defined percentage is about 20% of the total screen size.

[0086]FIG. 9 is a simplified schematic diagram of an image capturedevice having circuitry configured to extract an object of interestassociated with a low depth of field image in accordance with oneembodiment of the invention. Image capture device 150 includes lens 152which is capable of focusing on an object of interest. The object ofinterest and the associated background information is converted to adigital image through conversion block 164. The digital data is then bemanipulated in order to extract the object of interest. Here,microprocessor 153, e.g., an application specific integrated circuit, isconfigured to perform the extraction of the object of interest asdescribed herein.

[0087] Microprocessor 153 of FIG. 9 includes image extraction circuitry154. Image extraction circuitry 154 is made up of image featuretransformation circuitry 156 which is configured to generate an HOS mapas described above. Filtering circuitry 158 is configured to determine aboundary associated with the objects within the depth of field. Mergingcircuitry 160 is configured to analyze the frequency informationassociated with the HOS map to combine related homogenous regions of theHOS map. Merging circuitry 160 may also include circuitry capable ofexecuting the functionality of described above with reference toadaptive thresholding. Storage media 162 is provided for storage of theextracted object of interest. Of course, the code performing the featureextraction functions and the clustering/meta data generation functionscan be hard coded onto a semiconductor chip. One skilled in the art willappreciate that image extraction circuitry 154 can include logic gatesconfigured to provide the functionality discussed above. For example, ahardware description language (HDL) can be employed to synthesize thefirmware and the layout of the logic gates for providing the necessaryfunctionality described herein.

[0088] Image capture device 150 may be any image capture device, e.g., amicroscope, telescope, camera, video camera, etc. It should beappreciated that image extraction circuitry 150 may be integral to imagecapture device 150 or configured as a plug-in board. Similarly, storage162 may be included within image capture device 150 or separate. Thus,any microscopic, telescopic, or any low DOF image may be manipulated sothat an object of interest may be extracted. It should be furtherappreciated that the image capture device may be in communication with ageneral purpose computer capable of extracting an object of interest asdescribed herein.

[0089]FIG. 10 is a simplified schematic diagram of an image searchingsystem in accordance with one embodiment of the invention. Image capturedevice 150 is configured to capture digital image data in block 164through lens 152. The captured digital image data may be processed onimage extraction assembly 166, which is configured to extract theobjects of interest of the low depth of field image. It should beappreciated that image extraction assembly 166 may be a general purposecomputer in one embodiment of the invention. That is, image extractionassembly 166 performs the extraction of the object of interest accordingto the extraction schemes discussed herein. Image extraction assembly166 is in communication with content retrieval system 168. Contentretrieval system 168 is in communication with network 170. Thus, animage search over a distributed network may be performed based upon theextracted object of interest.

[0090] In summary, the embodiments described herein provide a method anda system that separates the pixels in the low DOF images into tworegions based on their higher order statistics. The low DOF image wastransformed into an appropriate feature space, which was called HOS mapin this paper. Morphological filter by reconstruction was applied tosimplify the HOS map. After the application of the morphological filter,a region merging technique was applied. Then adaptive thresholding isused for a final decision on a size associated with the object ofinterest.

[0091] It should be appreciated that by employing the powerfulmorphological tool for simplification, the proposed scheme performs welleven for focused smooth regions as far as their boundaries contain highfrequency components (i.e., edges). However, if the focused smoothregion is too large, the embodiments described herein may be lesseffective. This impediment may be solved if the algorithm is configuredto incorporate some semantic or human knowledge. It will be apparent toone skilled in the art that the proposed algorithm can be extended tovideo object segmentation in cooperation with the low DOF photographictechnique, since extracting video objects from arbitrary video sequencesis still highly challenging. Additionally, the embodiments describedherein may be applied to any suitable low depth of field images where itis desired to extract an object of interest, e.g., microscopy,photography, etc.

[0092] With the above embodiments in mind, it should be understood thatthe invention may employ various computer-implemented operationsinvolving data stored in computer systems. These operations includeoperations requiring physical manipulation of physical quantities.Usually, though not necessarily, these quantities take the form ofelectrical or magnetic signals capable of being stored, transferred,combined, compared, and otherwise manipulated. Further, themanipulations performed are often referred to in terms, such asproducing, identifying, determining, or comparing.

[0093] The above described invention may be practiced with othercomputer system configurations including hand-held devices,microprocessor systems, microprocessorbased or programmable consumerelectronics, minicomputers, mainframe computers and the like. Theinvention may also be practiced in distributing computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network.

[0094] The invention can also be embodied as computer readable code on acomputer readable medium. The computer readable medium is any datastorage device that can store data which can be thereafter read by acomputer system. The computer readable medium also includes anelectromagnetic carrier wave in which the computer code is embodied.Examples of the computer readable medium include hard drives, networkattached storage (NAS), read-only memory, random-access memory, CD-ROMs,CD-Rs, CD-RWs, magnetic tapes, and other optical and non-optical datastorage devices. The computer readable medium can also be distributedover a network coupled computer system so that the computer readablecode is stored and executed in a distributed fashion.

[0095] Although the foregoing invention has been described in somedetail for purposes of clarity of understanding, it will be apparentthat certain changes and modifications may be practiced within the scopeof the appended claims. Accordingly, the present embodiments are to beconsidered as illustrative and not restrictive, and the invention is notto be limited to the details given herein, but may be modified withinthe scope and equivalents of the appended claims. In the claims,elements and/or steps do not imply any particular order of operation,unless explicitly stated in the claims.

What is claimed is:
 1. A method for partitioning image data, comprising:defining an image feature space based upon frequency information;filtering image data of the image feature space with morphologicaltools; assigning a region of the filtered image feature space as aninitial object of interest; identifying a boundary of the initial objectof interest of the filtered image feature space; and determining a sizeof the initial object of interest relative to an image data size.
 2. Themethod of claim 1, wherein the method operation of identifying aboundary of the initial object of interest of the filtered image featurespace includes, calculating a normalized overlap boundary representing avalue indicating boundary pixels shared between the initial object ofinterest and a region bordering the initial object of interest; if thevalue is greater than a threshold value then the method includes,merging the region bordering the initial object of interest into theinitial object of interest.
 3. The method of claim 1, wherein the methodoperation of defining an image feature space based upon frequencyinformation includes, computing a higher order statistic (HOS) for eachpixel value associated with the image feature space.
 4. The method ofclaim 3, wherein the HOS is configured to calculate a fourth ordermoment associated with each pixel value.
 5. The method of claim 1,wherein the method operation of filtering image data of the imagefeature space with morphological tools includes, applying themorphological tools to the image data of the image feature space in amanner that preserves the boundary of the object of interest.
 6. Themethod of claim 1 wherein the method operation of filtering image dataof the image feature space with morphological tools includes, removingdark patches associated with focused regions of the image feature space;and removing bright patches associated with defocused region of theimage feature space.
 7. The method of claim 1, wherein the methodoperation of assigning a region of the filtered image feature space asan initial object of interest includes, identifying regions of the imagefeature space associated with a substantially constant frequency level;and assigning a value to each of the identified regions based upon thesubstantially constant frequency level, wherein the region of thefiltered image space associated with the initial object of interest isassigned a highest value.
 8. A method of image segmentation, comprising:generating a higher order statistic (HOS) map from image data; modifyingthe HOS map; determining a boundary associated with a focused region ofthe modified HOS map; and determining a final segmentation of thefocused region based upon a size of a value associated with the focusedregion relative to an image data size.
 9. The method of claim 8, whereinthe method operation of generating a higher order statistic (HOS) mapfrom image data includes, scaling a value associated with each pixel;and limiting a maximum of the value associated with each pixel.
 10. Themethod of claim 8, wherein the method operation of determining aboundary associated with a focused region of the modified HOS mapincludes; determining a value indicating a shared boundary amountbetween the focused region and a bordering region; and if the value isgreater than a threshold value, then the method includes, merging thefocused region and the bordering region.
 11. The method of claim 8,wherein the method operation of determining a final segmentation of thefocused region based upon a size of a value associated with the focusedregion relative to an image data size includes, decreasing a thresholdvalue until the size of the value associated with the focused regionrelative to the image data size becomes greater than about 20%.
 12. Themethod of claim 8, wherein the focused region is an object of interest.13. The method of claim 8, wherein the method operation of modifying theHOS map includes, applying reconstruction by erosion to pixel valuesassociated with the HOS map; and applying reconstruction by dilation tothe pixel values, the applying reconstruction by dilation including,restoring contours of components associated with the HOS map.
 14. Themethod of claim 8, wherein the method operation of determining a finalsegmentation of the focused region based upon a size of a valueassociated with the focused region relative to an image data sizeincludes, defining a threshold value; determining the size of the valueassociated with the focused region at the threshold value; and reducingthe threshold value until the size of the value associated with thefocused region is greater than about 20% the image data size.
 15. Themethod of claim 8, further comprising: identifying an interior of ahomogenous region of the modified HOS map; and assigning a marker to thehomogenous region.
 16. A method for extracting an object of interestfrom an image, comprising: defining an image feature space based uponfrequency information; and filtering the image feature space to smoothboth focused regions and defocused regions while maintaining respectiveboundaries associated with the focused regions and the defocusedregions.
 17. The method of claim 16, further comprising: merging similarfocused regions to define an object of interest (OOI); and determining asize of the OOI relative to the image feature space.
 18. The method ofclaim 16, wherein the method operation of defining an image featurespace based upon frequency information includes, calculating a higherorder statistic (HOS) for each pixel value of the image; and defining anHOS map from the calculated HOS.
 19. The method of claim 16, wherein themethod operation of filtering the image feature space to smooth bothfocused regions and defocused regions while maintaining respectiveboundaries associated with the focused regions and the defocused regionsincludes, applying a morphological filter by reconstruction to the imagefeature space.
 20. A computer readable medium having programinstructions for image segmentation, comprising: program instructionsfor generating a higher order statistic (HOS) map from image data;program instructions for modifying the HOS map; program instructions fordetermining a boundary associated with a focused region of the modifiedHOS map; and program instructions for determining a final segmentationof the focused region based upon a size of a value associated with thefocused region relative to an image data size.
 21. The computer readablemedium of claim 20, wherein the program instructions for generating ahigher order statistic (HOS) map from image data includes, programinstructions for scaling value associated with each pixel; and programinstructions for limiting a maximum of the value associated with eachpixel.
 22. The computer readable medium of claim 20, wherein the programinstructions for determining a boundary associated with a focused regionof the modified HOS map includes; program instructions for determining avalue indicating a shared boundary amount between the focused region anda bordering region; and program instructions for merging the focusedregion and the bordering region if the value is greater than a thresholdvalue.
 23. The computer readable medium of claim 20, wherein the programinstructions for determining a final segmentation of the focused regionbased upon a size of a value associated with the focused region relativeto an image data size includes, program instructions for decreasing athreshold value until the size of the value associated with the focusedregion relative to the image data size becomes greater than 20%.
 24. Thecomputer readable medium of claim 20, wherein the program instructionsfor modifying the HOS map includes, program instructions for applyingreconstruction by erosion to pixel values associated with the HOS map;program instructions for applying reconstruction by dilation to thepixel values, the program instructions for applying reconstruction bydilation including, program instructions for restoring contours ofcomponents associated with the HOS map.
 25. The computer readable mediumof claim 20, further comprising: program instructions for identifying aninterior of a homogenous region of the modified HOS map; and programinstructions for assigning a marker to the homogenous region.
 26. Animage capture device, comprising: a lens configured to focus objectswithin a depth of field (DOF); an image recording assembly configured togenerate a digital image including the objects within the DOF from imageinformation received through the lens; the image recording assemblycapable of generating a higher order statistic (HOS) map of the digitalimage in order to extract the objects within the DOF from the digitalimage.
 27. The image capture device of claim 26, wherein the imagerecording assembly includes filtering circuitry configured to determinea boundary associated with the objects within the DOF.
 28. The imagecapture device of claim 26, wherein the HOS map defines a feature spacebased upon frequency information associated with the digital image. 29.The image capture device of claim 28, wherein the image recordingassembly includes feature transformation circuitry configured togenerate the HOS map and merging circuitry configured to analyze thefrequency information to combine related homogenous regions of the HOSmap.
 30. The image capture device of claim 26, wherein the image capturedevice is selected from the group consisting of a microscope, atelescope, a camera, and a video camera.
 31. An image searching system,comprising: an image capture device having a lens configured to focusobjects within a depth of field (DOF); an image extraction assembly incommunication with the image capture device, the image extractionassembly configured to extract the objects within the DOF; and an imagecontent retrieval system in communication with the image extractionassembly, the image content retrieval system configured to receive datacorresponding to the objects within the DOF, the image content retrievalsystem further configured to identify a match between the received dataand gathered image data.
 32. The image searching system of claim 31,wherein the image capture device is selected from the group consistingof a microscope, a telescope, a camera, and a video camera.
 33. Theimage searching system of claim 31, wherein the image extractionassembly is a general purpose computer.
 34. The image searching systemof claim 31, wherein the image extraction assembly is integrated intothe image capture device.
 35. The image searching system of claim 31,wherein the image content retrieval system includes, a databaseconfigured to store the gathered image data; and a database query systemconfigured to identify the match between the received data and gatheredimage data through comparison of a signature index associated with thereceived data and a signature index associated with the gathered imagedata.
 36. The image searching system of claim 31, wherein the objectsare objects of interest.